This article presents a pipeline that enables multiple users to collaboratively acquire images with monocular smartphones and derive a 3D point cloud using a remote reconstruction server. A set of key images are automatically selected from each smartphone’s camera video feed as multiple users record different viewpoints of an object, concurrently or at different time instants. Selected images are automatically processed and registered with an incremental Structure from Motion (SfM) algorithm in order to create a 3D model. Our incremental SfM approach enables on-the- y feedback to the user to be generated about current reconstruction progress. Feedback is provided in the form of a preview window showing the current 3D point cloud, enabling users to see if parts of a surveyed scene need further attention/coverage whilst they are still in situ. We evaluate our 3D reconstruction pipeline by performing experiments in uncontrolled and unconstrained real-world scenarios. Datasets are publicly available.

Cloud-based collaborative 3D reconstruction using smartphones

Fabio Poiesi
;
Paul Chippendale;Erica Nocerino;Fabio Remondino;
2017

Abstract

This article presents a pipeline that enables multiple users to collaboratively acquire images with monocular smartphones and derive a 3D point cloud using a remote reconstruction server. A set of key images are automatically selected from each smartphone’s camera video feed as multiple users record different viewpoints of an object, concurrently or at different time instants. Selected images are automatically processed and registered with an incremental Structure from Motion (SfM) algorithm in order to create a 3D model. Our incremental SfM approach enables on-the- y feedback to the user to be generated about current reconstruction progress. Feedback is provided in the form of a preview window showing the current 3D point cloud, enabling users to see if parts of a surveyed scene need further attention/coverage whilst they are still in situ. We evaluate our 3D reconstruction pipeline by performing experiments in uncontrolled and unconstrained real-world scenarios. Datasets are publicly available.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/312659
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